政府统计数据作为政府制定决策依据的来源和政策执行后成果评价的依据，在政府日常决策中起到了至关重要的作用。它就像一面镜子，吸收过去的历史去折射未来可能的发展路径。因此，加强政府统计数据质量的把关十分必要。而基层统计机构作为行政级别最小的统计机构，是政府把控统计数据质量的源头单位。在整个统计系统中，基层统计机构主要负责统计数据的采集和加工，是整个统计活动中的具体实施者和经手人，对所在行政区域内采集的统计数据进行汇总、审核，通过专业分析，形成特定的统计产品，成为了政府政策制定者的决策依据和参考标准。作为维护数据质量的重要关口，如果基层统计数据出现偏差，当数据经过政府部门层层汇总，最终的数据偏差便会逐渐放大，也就不能准确反映经济发展运行情况，当作为政策决策依据时，便会误导决策者的判断，影响政策执行效果。

近年来，由于受新冠疫情、中美贸易冲突等事件影响，经济发展受到一定影响。社会各界对于政府统计数据的关注度日益提高，但对于政府统计数据的质疑声也时有发生。基层统计机构作为政府统计数据质量的“看门人”理应发挥更加重要的作用。如何提升基层统计数据质量对于保障我国政府统计数据质量具有十分重要的意义。

本篇论文主要基于全面质量管理理论、大数据治理理论和新公共管理理论为理论基础，采用文献研究法、案例分析法、访谈法和比较分析法为分析手段，以A市B区统计数据质量管理为研究对象进行分析，通过梳理区级统计局在纳统企业业务培训，验收报表及审核验收，执法检查和统计诚信单位评选等为保障数据质量所做工作，在结合区情分析的基础上，深入研究挖掘影响B区统计数据质量提升的因素，进而总结分析在基层统计数据管理过程中主要存在的普遍问题，并努力探寻问题背后的成因，通过学习国外国内统计数据质量的管理经验，运用公共管理理论知识，在数据质量管理、统计法律法规制度制定等方面提出改进意见和建议，以希望可以为A市B区统计系统甚至A市统计系统中基层统计数据质量的提升提供借鉴和参考。

As the source of the government 's decision-making basis and the basis for the evaluation of the results after the implementation of the policy, the government statistical data plays a vital role in the government 's daily decision-making. It is like a mirror, absorbing the past history to reflect the possible future development path. Therefore, it is necessary to strengthen the quality control of government statistical data. As the smallest statistical institution at the administrative level, the grassroots statistical institution can be said to be the source unit for the government to control the quality of statistical data. In the whole statistical system, the grass-roots statistical institutions are mainly responsible for the collection and processing of statistical data. They are the specific implementers and operators in the whole statistical activities. They summarize and audit the statistical data collected in the administrative region, and form specific statistical products through professional analysis. Finally, it has become the decision-making basis and reference standard for government policy makers. As an important gateway to maintain data quality, if there is a deviation in the statistical data at the grass-roots level, when the data is summarized by the government departments, the final data deviation will gradually enlarge, and it will not accurately reflect the operation of economic development. When it is used as a policy decision-making basis, it will mislead the decision makers ' judgment and affect the effect of policy implementation

In recent years, due to the impact of events such as the new coronavirus epidemic and the Sino-US trade conflict, the community has paid more and more attention to government statistics, but doubts about government statistics have also occurred from time to time. As the ' gatekeeper ' of the quality of government statistical data, the grass-roots statistical institutions should play a more important role. How to improve the quality of grassroots statistical data is of great significance for ensuring the quality of government statistical data in China.

This paper is mainly based on the theory of total quality management, big data governance theory and new public management theory. It adopts literature research method, case analysis method, interview method and comparative analysis method as the analysis means, and takes the quality management of statistical data in District B of City A as the research object. Through combing the business training of district-level statistical bureaus in enterprises, acceptance reports and audit acceptance, law enforcement inspection and statistical integrity unit selection to ensure the quality of data, based on the analysis of regional conditions, the factors affecting the quality improvement of statistical data in District B are deeply studied and excavated. Then, it summarizes and analyzes the common problems existing in the process of grass-roots statistical data management, and tries to explore the causes behind the problems. By learning the management experience of foreign and domestic statistical data quality, using the theoretical knowledge of public management, it puts forward suggestions and suggestions on data quality management, statistical laws and regulations system formulation and other aspects, hoping to provide reference for the improvement of the quality of grass-roots statistical data in the statistical system of B District of A City and even the statistical system of A City.